Method and apparatus for processing biometric images

ABSTRACT

A method and apparatus for applying gradient edge detection to detect features in a biometric, such as a fingerprint, based on data representing an image of at least a portion of the biometric. The image is modeled as a function of the features. Data representing an image of the biometric is acquired, and features of the biometric are modeled for at least two resolutions. The method and apparatus improves analysis of both high-resolution images of biometrics of friction ridge containing skin that include resolved pores and lower resolution images of biometrics without resolved pores.

RELATED APPLICATION(S)

This application is the U.S. National Stage of International ApplicationNo. PCT/US2004/019713, filed Jun. 21, 2004, published in English, andclaims priority under 35U.S.C. §119 or 365 to U.S. ProvisionalApplication No. 60/480,008, filed on Jun. 21, 2003, U.S. ProvisionalApplication No. 60/519,792, filed on Nov. 13, 2003 and U.S. ProvisionalApplication No. 60/523,068, filed on Nov. 18, 2003. This application isrelated to the PCT Application entitled “Acquisition of High ResolutionBiometric Images” filed on Jun. 21, 2004, International Application No.PCT/US2004/019917. The entire teachings of the above applications areincorporated herein by reference.

BACKGROUND OF THE INVENTION

Growing concerns regarding domestic security have created a criticalneed to positively identify individuals as legitimate holders of creditcards, driver's licenses, passports and other forms of identification.The ideal identification process is reliable, fast, and relativelyinexpensive. It should be based on modern, high-speed, electronicdevices that can be networked to enable fast and effective sharing ofinformation. It should also be compact, portable, and robust forconvenient use in a variety of environments, including airport securitystations, customs and border crossings, police vehicles, point of saleapplications, credit card and ATM applications, home and officeelectronic transactions, and entrance control sites to secure buildings.

A well established method for identification or authentication is tocompare biometric characteristics of an individual with a previouslyobtained authentic biometric of the individual. Possible biometriccharacteristics include ear shape and structure, facial characteristics,facial or hand thermograms, iris and retina structure, handwriting, andfriction ridge patterns of skin such as fingerprints, palm prints, footprints, and toe prints. A particularly useful biometric system usesfingerprints for individual authentication and identification. (Maltoni,Maio, Jain, and Prabhakar, “Handbook of Fingerprint Recognition”,Springer, 2003, chapter 1, and David R. Ashbaugh,“Quantitative-Qualitative Friction Ridge Analysis”, CRC Press, 1999).

Fingerprints have traditionally been collected by rolling an inkedfinger on a white paper card. Since this traditional process clearlyfails to meet the criteria listed above, numerous attempts have beenmade to develop an electronically imaged fingerprint method to addressnew security demands. These modern methods typically use, as a keycomponent, a solid-state device such as a capacitive or optical sensorto capture the fingerprint image in a digital format. By using asolid-state imager as part of a fingerprint identification apparatus, afingerprint can be collected conveniently and rapidly during a securitycheck, for example, and subsequently correlated, in near real-time, topreviously trained digital fingerprints in an electronic database. Thedatabase can reside on a computer at the security check point, on asecure but portable or removable storage device, on a remotely networkedserver, or as a biometric key embedded into a smartcard, passport,license, birth certificate, or other form of identification.

The topological features of a typical finger comprise a pattern ofridges separated by valleys, and a series of pores located along theridges. The ridges are typically 100 to 300 μm wide and can extend in anumber of different swirl-like patterns for several mm to one or morecm. The ridges are separated by valleys with a typical ridge-valleyperiod of approximately 250-500 μm. Pores, roughly circular in crosssection, range in diameter from about 40 μm to 200 μm, and are alignedalong the ridges. The patterns of both ridges/valleys and pores arebelieved to be unique to each fingerprint. No currently availablecommercial fingerprint acquisition technique is able to resolve poresand ridge deviation details to a degree necessary to use this vastlylarger amount of information as a biometric key. Accordingly,present-day automatic fingerprint identification procedures use onlyportions of ridge and valley patterns, called minutiae, such as ridgeending-points, deltoids, bifurcations, crossover points, and islands,which are found in almost every fingerprint (Maltoni, Maio, Jain, andPrabhakar, “Handbook of Fingerprint Recognition”, Springer, 2003,chapter 3). Extraction and comparison of minutiae is the basis of mostcurrent automatic fingerprint analysis systems.

There are several important limitations with minutiae-based methods ofautomatic fingerprint analysis. In order to collect enough minutiae forreliable analysis a relatively large area, at least 0.50×0.50 inches,good quality, fingerprint, or latent image of a fingerprint must beavailable. Large prints are often collected by rolling an inked fingeron a white card, and subsequently scanning the inked image into anelectronic database. This manual procedure is an awkward and timeconsuming process that requires the assistance of a trained technician.Automated methods for collecting large fingerprints usually requiremechanically complicated and expensive acquisition devices. Large areafingerprints suffer from distortions produced by elastic deformations ofthe skin so that the geometrical arrangements between minutiae pointsvary from image to image of the same finger, sometimes significantly. Inaddition, forensic applications can involve small, poor quality, latentprints that contain relatively few resolved minutiae so that reliableanalysis based on a limited number of minutiae points is quitedifficult.

Minutiae comparison ignores a significant amount of structuralinformation that may be used to enhance fingerprint analysis. Since thetypical fingerprint contains between 7 to 10 times as many pores asminutiae, techniques that include both pores and minutiae should greatlyimprove matching compared to techniques that use only minutiae. Thishighly detailed information is referred to in the industry as “levelthree detail,” and is the basis of most forensic level analysis oflatent images left at a crime scene, where the latent does not containenough minutiae to make an accurate identification. Stosz and Alyea (J.D. Stosz, L. A. Alyea, “Automated system for fingerprint authenticationusing pores and ridge structures”, Proc. SPIE, vol 2277, 210-223, 1994)have confirmed this expectation by showing that the use of porescombined with minutiae improves the accuracy of fingerprint matching andallows successful analysis of relatively small prints. Their imagesensor used a common prism-based configuration, a high-resolution ChargeCoupled Device (CCD) video camera, and a macro lens to provide theresolution needed to image pores. After acquisition, the gray-scaleimages are converted to a binary format and then processed further toproduce a skeleton image from which minutiae and pores are identified.Fingerprints are compared by independent correlations between pores andminutiae extracted from the various images.

SUMMARY OF THE INVENTION

There is a need for a procedure that improves an analysis of bothhigh-resolution images of biometrics (e.g., fingerprints that includeresolved pores) and lower resolution images of biometrics (e.g.,fingerprints without resolved pores). The principles of the presentinvention fulfill this need by using identifying information in abiometric, which, in the case of a fingerprint, can include fingerprintridge shapes or profiles in addition to usual ridge contours and theposition, shape, and sizes of pores. Images to be analyzed may includebiometric images, such as fingerprints, (i) from an apparatuscustom-designed to capture such images either in real-time ornon-real-time, or (ii) from other apparatus (e.g., computer scanner)that scans crime scene latent images, as well as existing criminalarrest or civil-applicant background check records.

Accordingly, one embodiment of the principles of the present inventionincludes a method and apparatus for processing an image of a biometric,which, for purposes of illustration only, is described in detail hereinin reference to acquiring and processing an image of a fingerprint. Themethod and apparatus, referred to generally here as “system,” may applya gradient edge detection process to detect features in a biometricbased on data representing an image of at least a portion of thefingerprint. The system models the image as a function of thefingerprint features, which may include level three features. The modelsmay be referred to herein as “trained” models.

The system may construct a model for at least two resolutions: a lowresolution “outline” model and a high resolution “details” model. Theoutline model may generally show an edge topology of ridge features; thedetails model generally shows edge topology and specific ridgedeviations and locations and sizes of pores. The system may alsoconstruct a model for a third resolution, a “fine details” model. Thefine details model is used for precisely defining and locatingparticular biometric features more accurately than at the low or highresolutions, such as pores in a fingerprint image. It is this thirdresolution model that is used, for example, for pore matching inauthentication and identification processes in a fingerprintapplication.

In constructing the model of a fingerprint, the system may identify,outline, and extract ridge deviation detail and pore features. The ridgedeviation detail may include ridge contours, including scars, and thepore features may include position, shape, and sizes of pores.

Biometrics for which the system is adapted to acquire, model,preprocess, and process may include: ear shape and structure, facial orhand thermograms, iris or retina structure, handwriting, and frictionridge patterns of skin such as fingerprints, palm prints, foot prints,and toe prints.

The system may construct models at various resolution levels through aprocess of binning the original image data. In this process, the imagedata is divided into equal-sized, sub arrays of pixels. Each pixel subarray is subsequently represented in the model by a single pixel whosevalue is equal to the average pixel value in the corresponding subarray. The sizes of the sub arrays can be adjusted by a user of thesoftware to any appropriate value; typical examples follow for a CMOS orCCD sensor array, described below in reference to FIG. 4, and having anarray of 1024×1280 6 μm square pixels. “Outline” resolution models maybe constructed with a sub-arrays having a relatively large numbers ofpixels, for example 10 to 15, “details” resolution models may beconstructed with sub arrays having a relatively small number of pixels,for example 5 to 10, and “fine details” models may be constructed withsub-arrays having even fewer pixels, for example 2 to 5. The finedetails model may be used to locate and define particular biometricfeatures more accurately than at the low or high resolution; forexample, in the case of fingerprints, pores on ridges may be located anddefined in the fine details model.

The gradient determined by the gradient edge detection process may beestimated for each pixel of the model after applying a noise filter tothe image. The detection process may use a finite-differences process.The detection process may also include a series of steps, such as thefollowing: (a) after calculating the gradients, identifying and markingan image point as an edge point having a locally maximal gradient in thedirection of the gradient that exceeds a threshold; (b) identifyingneighboring edge points by finding nearest pixels to the original edgepoint that lie in a direction that is approximately perpendicular to thegradient direction that passes through the first edge point; (c) for thenearest pixels, determining gradient values and, for the pixel with agradient that is maximal along its gradient direction and has a valuethat exceeds a threshold, assigning the pixel to be the next edge point;(d) continuing either until the edge is terminated or the edge closeswith itself to form a continuous curve; (e) terminating the process atthe previously determined edge point if the gradient of a candidate edgepoint is less than the threshold; and (f) repeating the process untilall potential edge points have been considered.

The system may automatically distinguish biometric features from noise.In one embodiment, the noise is defined as features that have less thana minimum width or extend less than a minimum distance. In addition toautomatically distinguishing the biometric features from noise, thesystem may also support manual editing of features and/or manualselection of features that must be present for a successful match.

The system may model multiple regions of a single image of the portionof the biometric. For example, the models may be models of five regionsof the biometric, such as four quadrants of the biometric with smalloverlaps in each quadrant, and may also include a center portion thatoverlaps portions of each of the four quadrants. The system may allow auser to add, extend, or delete features and may allow a user to identifyspecific or unique features that must be present for a match. The systemmay also allow a user to adjust the size or position of the model(s)relative to the biometric. User interaction may be performed through aGraphical User Interface (GUI) supported by the system.

A useful aspect of this technique for edge detection is an ability todetect edges even if a portion or all of the image is significantly overand/or underexposed, as a few levels of gray difference are sufficientto determine a location of an edge. This allows for highly accuratematching even if the incoming image or portion of the image forcomparison is not properly exposed, which allows for minimal or noexposure correction.

The image may be a previously stored image, and the system may normalizethe scale or size of the previously stored image so that the scale issimilar to that of the trained model(s). This scaling calibration alsoallows highly accurate measurements to be taken for forensic purposes.Typical accuracy of such measurements may be better than 10 um.

In the case where the biometrics are fingerprints, the fingerprintfeatures may include ridge structure with ridge deviation detail.Further, for fingerprints and other biometrics, the system may displaythe image to a user with an overlay of indications of the biometricfeatures on the image or filtered biometric features according to aselectable criteria. Also, the system may automatically rotate the imageto a specified orientation for displaying to a user and can rotate andscale the image while performing a match. In one embodiment, the imageis a gray-scale image, and the gradient edge detection process is agray-scale gradient edge detection process.

The system may also include a database and add the image of a biometricor portion thereof to the database. The system may store the image andthe model of the image in the database. The image may be stored at fullsampled resolution in the database or be compressed prior to beingstored in the database. Preferably, if the image is compressed, thesystem compresses it in a lossless manner. The model may also becompressed prior to being stored in the database. The system may alsoencrypt the data or the model prior to storing them in the database.

The system may also store additional information with the image and/ormodel in the database. For example, the associated information mayinclude at least one of the following: identity of a person associatedwith the biometric; manufacturer, model, or serial number of theinstrument supplying the data representing the biometric; the dateand/or time of imaging the biometric; calibration data associated withthe instrument used to acquire the biometric; temperature at the timethe image was acquired; unique computer ID of the computer receivingimage data from the instrument acquiring the image of the biometric; orname of person logged onto the computer at the time the image wasacquired. The associated information may also include a photograph,voice recording, or signature of the person whose biometric is imaged.The associated information may also be a watermark, where the watermarkmay be identifying information (e.g., associated information asdescribed above) or anti-tampering information to determine whether theimage and/or model has been compromised. If compromised, the image andmodel are typically marked or removed from the database.

The system may also include techniques for authenticating and/oridentifying the person whose biometric is acquired. For example, thesystem may compare a previously stored model from a database to apresent image, where the biometric is of a person having a knownidentity or an unknown identity. A “previously stored model” may be amodel that has been stored, for example in a local or remote database,or is a model of a previously acquired image that has not been storedper se Similar usage of the “previously stored image” also appliesherein. The present image may be a live image, an image previouslystored in a local or remote database, a scanned image, or an image fromanother source, e.g., the National Institute of Standards and Technology(NIST) or Federal Bureau of Investigation (FBI) database. The system maycompare the biometric features in at least two steps: comparing outlinefeatures and, if a candidate match is determined, comparing detailsfeatures, and, if still a candidate match, then comparing pore features.In comparing outline features, the system may compare outline featuresof the previously stored model to outline features of the present imageto determine (i) whether the present image is a candidate for a match or(ii) whether the previously stored model is a candidate for a match. Incomparing the outlines features, the system may determine whether thecomparison exceeds a predetermined candidate threshold. If the presentimage is not a candidate for a match, the system may compare outlinefeatures of a next previously stored model to the outline features ofthe present image to determine whether the present image is a candidatefor a match and use the next previously stored model for detailscomparison if it is a match. If the previously stored model is not acandidate for a match, the system may compare outline features of a nextpreviously stored model to the outline features of the present image todetermine whether the next previously stored model is a candidate for amatch and, if it is a match, the system may use the next previouslystored model for details comparison.

If the system determines a candidate match of outlines features isfound, the system may compare details features of the previously storedmodel with detailed features of the present image. The system maycompare the details features by determining whether the detailscomparison exceeds a predetermined threshold or may determine whetherrequired features associated with the previously stored model are foundin the present image. In the case of biometrics related to frictionridge containing skin, the system may also determine whether porefeatures in the previously stored model are found in the present image.If so, the system may indicate which pores in the previously enrolled(i.e., acquired and modeled) image appear in the expected location inthe present image, including allowance for distortions that normallyoccur between successive impressions, and may also show a pore count ora statistical probability of an error in such a match. The system, incomparing the outline, details, required details, and pore features, maydetermine whether the comparison meets a predetermined level of a numberof consecutive frames in which the various features thresholds have beenmet, in order to call the comparison a match. The individual detailsand/or pores from successive frames need not be the same details andpores (unless specified as required) but could be different, but alsoexceeding the threshold(s). Further, the system may select anotherpreviously stored model for correlating with the feature set of thepresent image, and a successful match declared if any model or modelsexceed the threshold(s).

The system may also scale and/or rotate the previously stored model(s)present image, or model of the present image for use in comparing thetwo.

The system may also adaptively adjust the previously stored model(s) toaccount for variability associated with recording or acquiring thepresent image due to elasticity of the skin. For example, thevariability may include stretching of the fingerprint, or portionsthereof, laterally, longitudinally, or radially. The variability mayalso be caused by pressure of the fingerprint on a medium used to recordor acquire the present image. In addition, this adaptive conformity mayalso take into account an expected location of ridge deviation detailsand, optionally, pore details.

The system may also compare the previously stored model against multiplepresent images until a match is found or comparison with the multiplepresent images is complete. In another embodiment, the system maycompare multiple previously stored models against the present imageuntil a match is found or comparison with the multiple previously storedmodels is complete. In yet another embodiment, the system may comparemultiple previously stored models against multiple present images untila match is found or comparison among the multiple present images and themultiple previously stored models is complete.

In some embodiments, the present image includes multiple fingerprints ofan individual. For example, between two and ten fingerprint images of anindividual may be captured and modeled.

The system may also provide for preprocessing of the data representingthe image. The preprocessing may include subsampling the image tocapture the data. The preprocessing may also include decimating the datarepresenting the image, where decimating may include removing everyother row and every other column of the data. The system may alsopreprocess the data by binning the data, which includes averagingmultiple “nearby” pixels to reduce the data to a predetermined size. Thesystem may also correct for uneven imaging of the fingerprint, sometimesreferred to as “flattening the field.” Flattening the field compensatesfor light source properties, optics variations, Holographic OpticalElement (HOE) variations, and differences among the gain and/or offsetsof the pixels in a sensor array used to image the fingerprint. Thesystem may also account for defective pixels in the sensor array, forexample, by averaging pixel values around a defective pixel to determinea corrected intensity value of the defective pixel. The location anddescription of defective pixels may be provided by the manufacturer ofthe sensor array or measured during sensor assembly and stored in systemmemory for use during calibration of the fingerprint sensor.

The preprocessing may also include encrypting the data representing theimage. The preprocessing may also include changing the imageorientation, such as horizontal or vertical flip and rotation, or acombination of the two. The system may also apply a watermark to thedata representing the image or the system may attach acquireinformation, such as information about the instrument acquiring theimage representing the biometric, to the data representing the image.The watermark may contain information and also may be used as atamper-proofing technique, ensuring that a modified image is identifiedor not allowed to be used.

Another embodiment of the system according to the principles of thepresent invention includes an acquisition unit that acquires datarepresenting an image of a biometric. The acquisition may be a biometricsensor to acquire live scan images, a photograph scanner to scan paperprints of biometrics, a computer modem to receive data from a databaseof biometric images, or other electronic medium performing similarfunctions. The system also includes a modeler that models features ofthe fingerprint utilizing at least two levels of image resolution.

Various example embodiments of the instrument used to acquire images ofbiometrics are described herein. The embodiments may also includealternative embodiments, such as those disclosed in a relatedapplication, entitled “Acquisition of High Resolution Biometric Images,”Attorney Docket No. 3174.1012-004, being filed concurrently herewith.The entire teachings of the related application are incorporated hereinby reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescription of preferred embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the invention.

FIG. 1 is a computer network in which a fingerprint sensor according tothe principles of the present invention is deployed;

FIG. 2 is a system hierarchical diagram of the fingerprint sensor ofFIG. 1;

FIG. 3A is a high level schematic diagram of the fingerprint sensor ofFIG. 1;

FIG. 3B is a detailed schematic diagram of the fingerprint sensor ofFIG. 1;

FIG. 4 is a generalized mechanical diagram of an imager in thefingerprint sensor of FIG. 1;

FIG. 5 is an electrical schematic diagram of camera electronics in acamera of the fingerprint sensor of FIG. 1;

FIG. 6A is a hierarchical diagram of a local computer used in thecomputer network of FIG. 1;

FIG. 6B is a block diagram of example software executed in the localcomputer of FIG. 1;

FIG. 7 is a flow diagram of a process associated with acquiring andprocessing of the fingerprint image data acquired by the fingerprintsensor of FIG. 1;

FIG. 8 is a high level block diagram of example processing elementsoperating in the computer network of FIG. 1 for processing the acquiredfingerprint images;

FIG. 9 is an image of a portion of a fingerprint acquired by thefingerprint sensor of FIG. 1;

FIG. 10 is the image of FIG. 9 with an overlay of outline features offingerprint ridges;

FIG. 11 is the image of FIG. 9 with a subset of the fingerprint overlaidwith detail features of the fingerprint ridges and deviations;

FIG. 12 is a zoomed-in image of the portion of overlaid features of FIG.11;

FIG. 13 is a flow diagram of a fingerprint enrollment process executedby the enrollment software of FIG. 6B;

FIG. 14 is a flow diagram of acquisition software of FIGS. 6B and 13;

FIG. 15 is a flow diagram of a process for constructing a model of thefingerprint acquired by the processes of FIGS. 13 and 14;

FIG. 16 is a flow diagram of fingerprint verification that is part ofthe analysis software of FIG. 6B;

FIG. 17A-17C are flow diagrams of a matching processes beyond theoutlines level executed by the fingerprint verification process of FIG.16;

FIG. 18 is a flow diagram of a fingerprint feature correlation processexecuted by the processes of FIGS. 16 and 17;

FIG. 19 is flow diagram of an adaptive conformity process executed bythe fingerprint feature correlation process of FIG. 18; and

FIGS. 20A and 20B are diagrams including a set of pixels in the sensorarray of FIG. 3A that illustrates preprocessing for correcting badpixels.

DETAILED DESCRIPTION OF THE INVENTION

A description of preferred embodiments of the invention for afingerprint biometric follows. It should be understood that theprinciples of the present invention and example preferred embodiments ofthe methods and apparatus described below may be applied to otherbiometrics, including: ear shape and structure, facial or handthermograms, iris or retina structure, handwriting, and friction ridgepatterns of skin such as fingerprints, palm prints, foot prints, and toeprints

In general, the principles of the present invention include a procedurethat is capable of identifying highly detailed fingerprint features byusing gradient edge detection techniques at several image resolutions.The procedure identifies and traces edges of structural features tooutline ridges and pores. At sufficiently high resolution, referred toin the industry as “level three details,” the ridge outlines containthousands of structural features that can be used in fingerprintmatching. This capability improves matching reliability over systemsthat reduce ridge patterns to a few minutiae types or systems thatconsider ridges only as simple contour lines, via a process known asbinarization or thinning. Because of the richness of features infingerprints at high resolution, the procedure also allows for reliablematching of small portions or fragments of prints. In a preferredembodiment, edge detection software is combined with high resolutionimage acquisition technology that is capable of resolving pores andridge profiles.

FIG. 1 is a system diagram in which an embodiment of a fingerprintsensor 100 according to the principles of the present invention isemployed. The fingerprint sensor 100 includes a fingerprint imager 110and fingerprint camera 120. The imager 110 and camera 120 may bemechanically, electrically, and optically connected in a single “box.” Afinger 105 is placed on the fingerprint imager 110 at a “viewable”location by the imager 110 for acquisition of a fingerprint 115 by thecamera 120 and for modeling of the fingerprint 115 by processing asdescribed hereinbelow.

For many reasons, it is useful to design the fingerprint sensor 100 inas small a package as possible, such as for use in field operations,security systems, and other applications. However, although packaged ina small size, the fingerprint imager 110 and camera 120 are preferablydesigned in such a manner as to capture an image of the fingerprint 115in high resolution. One way to achieve a small packaging size is throughnovel optical design. For example, the imager 110 may include aHolographic Optical Element (HOE). The HOE allows the fingerprint camera120 to be positioned close enough to the fingerprint 115 being imaged toreceive, without use of large collecting optics, high resolution imagefeatures of the fingerprint 115

Although a holographic optical element allows for minimizing the size ofthe fingerprint imager 110 and, consequently, the fingerprint sensor100, the HOE is generally temperature sensitive. Therefore, compensatingfor the temperature sensitivity of the HOE is useful for acquiringaccurate, high-resolution images of the fingerprint 115. Compensatingfor the temperature sensitivity of the HOE can be passive or active andis discussed further beginning in reference to FIG. 2.

Continuing to refer to FIG. 1, the fingerprint camera 120 includes aninterface to communicate bidirectionally with a local computer 130 via acontrol channel/data link 125. The fingerprint camera 120 sends imagedata 160 to the local computer 130, and the local computer 130 may sendcontrol data 165 or other information, including image data 125, to thefingerprint camera 120 or imager 110 via the link 125.

The local computer 130 may include a variety of processing capabilities,such as modeling, authentication, and authorization, that is applied tothe image data 160. The local computer 130 may be in communication witha local database 135 via a local link 132. Image data and associatedmodel(s) 170, collectively, are communicated between the local computer130 and local database 135 via the local link 132. Other data, such asadministrative data, may also be communicated over the local link 132for storage in the local database 135 for later retrieval.

The local computer 130 may also communicate with a remote computer 150via a computer network 140, such as the Internet. The image data andassociated model(s) 170 are communicated via network communicationslinks 145 among the local computer 130, computer network 140, and remotecomputer 150. The remote computer 150 is in communication with theremote database via a remote database link 152.

The remote computer 150 may include some or all of the processing of thelocal computer 130 or include other services, such as remote retrievalof image data and associated model(s) 170 from a remote database 155 orauthentication of a live scan image of a fingerprint.

FIG. 2 is a hierarchical diagram of the fingerprint sensor 100. Thefingerprint sensor 100, as discussed in reference to FIG. 1, includes afingerprint imager 110 and fingerprint camera 120. Each will bediscussed in turn.

The fingerprint imager 110 includes, optics 210, and, optionally, activecontrol circuits/element(s) 225. The optics 210 includes a light source205, optical elements 250, which are non-HOE's such as a waveguide andlens(es), and at least one HOE 410, which includes a hologram.

The light source provides a collimated and expanded beam of light 207.The light source includes one or more beam shaping optical elements, andmay include a coherent source, such as a laser diode, which worksefficiently with a HOE, or a non-coherent source.

The optional active control circuit/element(s) 225 may include an anglecontroller 230 and actuator 235. The actuator may be a Direct Current(DC) motor, stepper motor, piezo-electric actuator, or otherelectro-mechanical device capable and adaptable for use in moving thelight source 205 at angles fine enough for use in the fingerprint sensor100. A wavelength controller 240 may also be employed in the imager 110,where the wavelength controller 240 may be used to change the wavelengthof the light source 205 in order to compensate for temperature-inducedchanges in the Bragg condition of the HOE. A power controller 245 mayalso be employed by the imager 110 to control the output power of thelight source 205 for controlling exposure levels of the fingerprint 115.

The fingerprint camera 120 includes a sensor array 215 and electronics220. The sensor array 215 may be a Charge Coupled Device (CCD) or aComplementary Metal Oxide Semiconductor (CMOS), for example, and have anumber of pixels providing a resolution fine enough for use in thefingerprint sensor 100. The electronics 220 are coupled to the sensorarray 215 for receiving pixel data for processing. The electronics mayinclude a processor, memory, and sensor data communications interface.

It should be understood that the hierarchical diagram of FIG. 2 ismerely exemplary and could be configured in other ways and includeadditional or fewer components for implementing the principles of thepresent invention.

FIG. 3A is a generalized schematic diagram of the fingerprint sensor 100and includes a subset of the components introduced in FIG. 2. The imager110 of the fingerprint sensor 100 includes the light source 205projecting a light beam 207 into the optics 210. An actuator 390 may bemechanically connected to the light source 205, to the optics 210 or toboth, directly or indirectly, to direct the light beam 207 into theoptics 210 in a controlled angular manner. Active control circuit(s) 225provide(s) control signal(s) 389 to the actuator 390 and/or the lightsource 205 in accordance with the descriptions above in reference toFIG. 2. The active control circuit(s) may receive feedback from theactuator 390 or light source 205 for control or regulation purposes.

In this embodiment, a feedback signal 391 is presented to the activecontrol circuit(s) 225 by the camera electronics 220. As in the case oftypical feedback control systems, the feedback signal 391 is generatedby the camera electronics 220 as a function of a difference between anactual signal level and a desired signal level corresponding to imagingperformance. In the case of the fingerprint sensor 100, the feedbacksignal 391 may represent a deficiency in light intensity emitted by thelight source 205, or may represent an angular error of the light beam207 projecting onto the optics 210, where the angular error may becaused by temperature effects on the HOE. The camera electronics 220 maydetermine the feedback signal 391 based on the image data 160, subsetthereof, or other signal provided by the sensor array 215. Otherphotosensitive areas 380 outside the active pixel field of the sensorarray 215 may provide a signal 382, to the camera electronics 220, fromwhich the feedback signal 391 is derived.

The camera electronics 220 may also provide a control signal 165 to thesensor array 215 for use during imaging of the fingerprint featuresimage 302. Further, the camera electronics 220 also includes aninterface (not shown) for communicating with the local computer 130 viathe communications link 125 for transferring the image data 160.

FIG. 3B is a detailed schematic diagram of the fingerprint sensor 100. Abrief description of the imager 110 and camera 120 is described in turn.

The imager 110 includes a power control circuit 245, angle controlcircuit 230, and wavelength control circuit 240. The power controlcircuit 245 provides feedback signals to the light source 205 via aninterface 393. Similarly, the wavelength control circuit 240 providesfeedback to the light source 205 via an interface circuit 398. The anglecontrol circuit 230 provides a signal to the actuator 235 via aninterface 396.

The optics 210 includes optical elements 250 and at least one HOE 410.The optical elements 250 and HOE 410 are arranged in a manner adaptedfor imaging the features of the fingerprint 115. Details of thearrangement between the optical elements 250 and HOE 410 are describedin detail beginning in reference to FIG. 4.

Referring now to the details of the fingerprint camera 120, theelectronics 220 include multiple electrical components, including: logic330, microprocessor 335, microprocessor memory 355, system memory 345,interface circuit 360, and Analog-to-Digital Converter (ADC) 322, inembodiments where the sensor array 215 outputs data in the form ofanalog signals. The microprocessor 335 may be integrated into the logic330 or may be a separate component communicating with the logic 330 overa bus (not shown). The logic 330 may be a Field Programmable Gate Array(FPGA) or other logic device or a processor adapted for performing thefunctions described herein with regard to the logic 330.

Communication between the sensor array 215 and the logic 330 occurs viaa control interface 325, data bus 320, and, in certain cases, analternate data line 385. Data is ‘read out’ of the sensor array 215 viathe data bus 320 at a rate between 1 MHz and 60 MHz, in someapplications, but may be increased or decreased based on the applicationand technological limits. In this embodiment, an additionalphotosensitive area 380 outside the active pixel field of the sensorarray 215 may provide a feedback signal 382 via the line 385 to thelogic 330 for use in providing the power feedback 392 to the powercontrol circuit 245, the angle feedback 395 to the angle control circuit230, or the wavelength feedback 397 to the wavelength control circuit240, or any combination thereof. The logic 330 may be designed toreceive signals from a subset of pixels in the sensor array 215 for usein computing an angle feedback signal 395, wavelength feedback signal397, or power feedback signal 393, or any combination thereof. The logic330 or microprocessor 335 may determine the feedback signals 391 (i.e.,power feedback 392, angle feedback 395, or wavelength feedback 397)through use of various techniques, such as a Least-Means-Square (LMS)technique, optimization techniques, intensity differencing technique, orother process useful for determining single- or multi-variable control.

Continuing to refer to FIG. 3B, the microprocessor 335 communicates tothe microprocessor memory 355 via a microprocessor memory bus 350. Thelogic 330 communicates with system memory 345 via a system memory bus340. The system memory 345 may communicate with the interface circuit360 via a memory/interface bus 365. The interface circuit 360communicates with the logic 330 via a logic/interface control bus 370and logic/interface data bus 375. The interface circuit 360 communicateswith the local computer 130 via a local bus 125, which includes controllines 362 and data lines 364.

In operation, the light source 205 produces an expanded, collimatedoptical beam 207 that is projected by the optical element 450 and HOE410 to reflect off a cover plate 420 for imaging the features of thefingerprint 115 by the sensor array 215. The sensor array 215 outputsdata representing an image of at least a portion of the fingerprint 115to the logic 330 via the data bus 320 at a sampling rate of, forexample, 40 MHz. The logic 330 directs the image data 160 to differentplaces in various embodiments. For example, in one embodiment, the logic330 directs the image data 160 to the system memory 345 for additionalprocessing or directs the image data 160 to the interface circuit 360via the logic/interface data bus 375 for direct transmission to thelocal computer 130. The logic 330 may also direct a portion of the imagedata 160 to the microprocessor 335 for determining the feedback signals391 in an embodiment in which active feedback control is employed in thefingerprint sensor 100.

FIG. 4 is a schematic diagram of the imager 110. Referring first to theimager 110, the light source 205 produces coherent, expanded beam oflight 207 at a wavelength of, for example, 655 nm. The light beam 207enters the optical element 405 at an angle that causes the light 207 tobe guided through the waveguide 405 by total internal reflection at thesubstrate-air interfaces. The light beam 207 encounters an interfacebetween a Holographic Optical Element (HOE) 410 and the substratewaveguide 405, at which point, a portion of the light beam 207 isdiffracted by the HOE, which includes a holographic grating, at a nearnormal angle to the guide surface and travels through a cover plate 415to the finger 105. Fresnel reflection of the diffracted light at theinterface of the cover plate 415 and fingerprint 115, referred to hereinas a “finger contact surface”, directs some of the diffracted light backthrough the HOE 410, through the substrate waveguide 405, and onto theentrance face (i.e., sensor array 215) of the camera 120. Reflection atthe cover plate is suppressed at locations where objects come intooptical contact with the cover plate. The remaining reflected lightcarries an image of these contact areas to the camera 120. The imagedata 160, which represents an image of the fingerprint 115, is directedacross the data bus 125 to the local computer 130 in a manner asdiscussed above in reference to FIG. 1.

One example embodiment of the fingerprint imager 110 is constructed asfollows. The light source 205 is a laser diode that emits 5 mW of lightat 652 nm. The substrate (e.g., glass) waveguide 405 has an entranceface for the laser light 207 that is beveled to an angle of about 60degrees from the horizontal. The dimensions of the waveguide 405 are 36mm long, 2.5 mm thick, and 25 mm wide. The cover plate is 1 mm thick andhaving a square surface of 25×25 mm. In this example, the image of thefingerprint 115 is captured by a CMOS electronic imager having a 1024 by1280 array of 6 μm square pixels and 256 gray levels. The size of theresulting image is 6.1 mm by 7.7 mm, while its resolution is 167 pixelper mm or 4200 pixels per inch.

In operation, when the finger 105 is pressed onto the finger contactsurface 420, the ridges of the fingerprint 115 make optical contact andsuppress reflection. Since pores are depressions along the ridges, thereis reflection from the cover plate at pore locations. The resultingimage of the fingerprint 115 that is presented to the camera 120includes light colored areas for the valleys and dark ridges with lightpores aligned along the ridges. An undistorted, high-resolution image ofthe fingerprint 115 can be captured by the camera if the light beam 207that is diffracted by the HOE 410 is collimated and has a uniformwavefront.

FIG. 5 is a schematic diagram of a subset of the camera electronics 220.This schematic diagram provides a layout of the sensor array 215 and theFPGA 330. The size of these components 215, 330 results in a particularlayout in which the finger 105 is positioned on top of the imager 110 ina manner such that the fingerprint image 500 acquired by the sensorarray 215 may be mis-oriented in a way that inverts the left-rightorientation of the image, for example, or rotates the image to aposition clockwise from the desired viewing position, for example. Theimage is preferably, and in some cases must be, acquired and displayedin the same orientation as observed when inked and rolled on a whitecard, to preserve the traditional orientation used to store and comparepre-existing law enforcement image databases. Therefore, the FPGA 330may be programmed to change the image orientation through predefineddata manipulation. For example, the system memory 345 may be designed tostore two or more times the amount of image data 160 so as to allow theFPGA 330 to rewrite the image data within the memory in a manner forreading-out the data to the local computer 130, which allows thefingerprint image 500 to be displayed to a viewer in a standard “tip up”orientation. In the case of a large system memory, the size of thesystem memory 345 also allows for storage of multiple data sizefingerprint images 500 for buffering or averaging purposes. It should beunderstood that the system memory 345 may be larger or smaller than justdescribed depending on the particular application.

FIG. 6A is a hierarchical diagram of the local computer 130. The localcomputer 130 includes a sensor data/control interface 605, software 610,display 630, local storage 635, and network interface 640. The software610 includes processing software 615, a Graphical User Interface (GUI)620, the local database 135, and sensor control software 625. The localcomputer 130 can be a standard computer that is customized to operatewith the fingerprint sensor 100 or can be a custom-designed computerthat includes specific, optimized hardware, such as a specificsensor/data control interface 605 to optimize performance with thefingerprint sensor 100.

FIG. 6B is a block diagram of example processing software 615 that maybe loaded and executed by the local computer 130. The processingsoftware 615 may include acquisition software 645 (FIG. 14), enrollmentsoftware 650 (FIGS. 13-15), analysis software 655 (FIGS. 16-20),administrative software 660 (e.g., database administration), andadditional information processing software 665 (e.g., security features,header information, and watermarking). Details of the processingsoftware 615 are described in detail below in reference to the indicatedassociated figures.

FIG. 7 is a system level flow diagram of a process 700 for (i) imagingthe fingerprint 115 with the fingerprint sensor 100 through (ii)processing image data 160 representing an image of at least a portion ofthe fingerprint. The process 700 begins with the fingerprint sensor 100capturing fingerprint image data (step 705). The image data 160 ispresented to the fingerprint camera processor 335 or local computer 130for preprocessing (step 710). Preprocessing can help speed systemperformance significantly, while maintaining more cost effective imagerto computer interfaces that might otherwise be bandwidth limited.Preprocessing 710 may include changing the image orientation,calibrating the image, scaling the image, flattening the field acrossthe pixels of the sensor array 215, correcting for defective pixels ofthe sensor array 215, decimating the captured image data 160, applyingencryption to the image data, applying a watermark to the image data,adding header information to the image data, or compressing the data,optionally through a lossless compression technique. Other forms ofpreprocessing may also be applied for transmission and/or storage withthe image data 160. The image data and, optionally, the additional data(collectively 725) is provided to the fingerprint camera processor 335,local computer 130, or remote computer 150 for processing the image data160 (step 715). Examples of processing the image data 160 include:modeling the features of the fingerprint, enrolling the fingerprint,authenticating the individual associated with the data representing atleast a portion of the fingerprint, and identifying the individual. Theprocess 700 ends in step 720.

FIG. 8 is a flow diagram of a process 800 for modeling the image as afunction of the fingerprint features. The image data 160 is processed bya gradient edge detector 805. The gradient edge detector provides theimage data and fingerprint features (collectively 815) to a modeler 810.The modeler generates a model of the fingerprint as a function of thefingerprint features. The modeler 810 outputs image data and model(s)(collectively 170) to a local database 135 or other processor, such asthe remote computer 150 for additional processing or storage in theremote database 155, for example.

FIGS. 9-12 include images of fingerprints 115 that are (i) displayed toa user, (ii) modeled, (iii) processed (e.g., authenticated, identified,etc.), or (iv) stored for later retrieval. FIGS. 13-20 include exampleprocesses that improve the analysis of both high-resolution fingerprintsthat contain resolved pores and lower resolution fingerprints withoutresolved pores. The processes of FIGS. 13-20 do so by using identifyinginformation in a fingerprint, which can include ridge shapes or profilesin addition to the traditional ridge contours, and the position, shapeand prominence of pores. Before describing the processing, a briefdescription of the fingerprint images and overlays indicatingfingerprint features determined by the processing is described.

FIG. 9 is an example of a fingerprint image 900, represented by theimage data 160, that was acquired with the fingerprint sensor 100. Lightcolored (e.g., gray) valleys (V) 905 and dark ridges (R) 910 areobservable. Examples of pores (P) 915, ridges 910, ridge details (RD)920, and minutiae (M) 925 are all indicated in the figure. Some or allof these types of features can be used by the processes of FIGS. 13-20to compare and match fingerprint images 900. The fingerprint image 900contains fewer than ten minutiae 925 and almost ten times as many pores915 as minutiae 925.

FIG. 10 is an example of the image data 160 including indications offeatures obtained at the “outline” resolution level. Thick lines 1005trace identified boundaries between ridges 905 and valleys 910 andoutline some of the pores 915. Features that are outlined in thick lines1005 are considered real and are used for subsequent fingerprintmatching steps. Noise is traced in thin lines 1010 and is not used formatching. A trained model of a single fingerprint includes one or moreof these feature sets from various regions of the complete fingerprintimage data 160.

FIG. 11 is an example of the fingerprint image data 160 with a smallercentral region 1105 containing highlighted features that were identifiedusing gradient edge detection procedures, discussed below beginning atFIG. 13. The features were obtained at the “details” level ofresolution. Similar to FIG. 10, thick lines 1110 indicate “real” (i.e.,non-noise) features and thin lines 115 indicate noise.

The local computer 130 may display the image data 160 in the form of thefingerprint image 900 in the GUI 620. Through use of standard or customGUI techniques, the user can, for example, (i) select specific featuresthat must be present for a match, (ii) move the modeled region 1105 inany direction to model different area, (iii) enlarge or reduce the sizeof the modeled region, or (iv) select or deselect features within theregion to reduce noise from being part of the model, or to includespecific features within the region.

FIG. 12 is an expanded view of the model region 1105 from thefingerprint shown in FIG. 11 that includes indications of features ornoise operated on by the user via the GUI 620. In the fingerprint image900, the thick gray lines 1110 are real, and the black lines 115 arenoise. However, the user may want to change the results produced by theautomated processing. For example, the user may use a Graphical UserInterface (GUI) to deselect certain features 1205 he or she may considernoise even though the automated processing considered the features 1205real fingerprint features. The manual selection process may also work inreverse, where features the automated processing considers noise may bereselected by the user to be included as real fingerprint features 1210through use of the GUI 620.

There are thousands of features included in the enrolled modelinformation set. A later comparison to these features that finds, forexample, 80% of the total enrolled features means that 4000 separatefeatures were found that match an original enrolled image of 5000 totalfeatures in an area as small as 5×5 mm, for example.

In addition, the user may define specific features 1215, or a percentageof specific features as being required, in addition to a percentage ofall features, as qualification criteria for a match. The more features1215 that are required for a match reduces the likelihood of falsedetections but increases the likelihood of missed detections. Therefore,a threshold number of required features 1215, such as 80%, may bespecified to account for noise, distortions, lighting or otherimaging-related effects.

Multiple biometrics (e.g., multiple fingerprints of an individual;fingerprint(s) and palm prints; fingerprint(s) and iris scan; and soforth) may be acquired and modeled. The multiple biometrics may becombined in various manners, such as root-mean-square or weighted sum,to for a “combined metric.” The combined metric may be used for latercomparing of the acquired multiple biometrics with future acquiredimages (e.g., live scans). Combining multiple biometrics may improvesystem reliability.

The image data and model 170, collectively, may be transmitted as apaired set from the local computer 130 to the local database 135 orremote computer 150 as discussed above in reference to FIG. 1. Inapplications where the smallest possible model is required, on asmart-card or drivers license, for example, only the model, without theaccompanying image may be used. The outline features, details features,required features 1215, and fine details (e.g., pores) features may bestored as part of the model and used during later processing.

Referring now to processing aspects of the present invention, two majorsteps are described: fingerprint enrollment (FIGS. 13-15) andfingerprint matching (FIGS. 16-19). During enrollment, a high-resolutiondigital fingerprint (e.g., image data 160 representing the fingerprintimage 900) is acquired, and one or more sets of features to be used forsubsequent fingerprint matching are identified. Fingerprint matchingcompares these sets of features (e.g., ridge deviation details, such asthe required features 1215, pores, and minutiae) to the unknownfingerprints and decides if the two fingerprints are from the sameindividual. The source for these two fingerprints can be a database ofhigh-resolution fingerprints, a live scan image, a legacy database oflower resolution fingerprints, latent fingerprints (i.e., fingerprints“lifted” at a crime scene), or combinations of these sources. Althoughthe invention makes use of the extensive data available with higherresolution images, the techniques also work with lower resolutionimages.

The processes of FIGS. 13-20 describe an automatic fingerprintrecognition method that identifies fingerprint features by usinggradient edge detection techniques. In one embodiment, the features areacquired in gray-scale. In another embodiment, color may be part of theacquired image. The techniques identify and trace edges of structuralfeatures to outline ridges and pores. At sufficiently high resolution,the ridge outlines contain thousands of unique structural features(i.e., level three features) that can be used in fingerprint matching.This capability improves matching reliability over systems that reduceridge patterns (by binarization and thinning) to a few minutiae typesBecause of the richness of features in fingerprints at high resolution,the method and apparatus according to the principles of the presentinvention produces reliable, high speed matching of small prints to amuch higher degree of accuracy than with standard minutiae-only basedsystems.

The processes of FIGS. 13-20 analyze an unaltered gray-scale image ofthe fingerprint 115 to reduce both computing time and false acceptrates. In a preferred embodiment, edge detection software is combinedwith high resolution image acquisition technology that is capable ofresolving pores and ridge profiles.

FIG. 13 is a flow diagram of an example process 1300 for enrolling afingerprint 115. The fingerprint 115 is first acquired (step 1305). Anexample process for acquiring an image is provided in FIG. 14.

FIG. 14 illustrates a flow diagram of an example process 1400 foracquiring an image of the fingerprint 115, described in reference toFIG. 4. A direct light beam 207 is transmitted through a substratewaveguide 405 (step 1405). The finger 105 is pressed onto a contact areaof a coverplate (step 1410). The exposure time of the fingerprint camera120 is adjusted (step 1415) either manually or automatically. Thefingerprint image 302 (FIG. 3A) is captured (step 1420), and the process1400 returns (step 1425) to the process 1300 of FIG. 13 for furtherprocessing.

Referring again to FIG. 13, after acquisition, the image data 160 istransferred (step 1310) to the local or remote networked computers 130,150, respectively, by one of a number of possible connecting protocols,such as, but not limited to, IEEE 1394 (Firewire), USB, Serial ATA,fiber-optic cable, any applicable wireless communications protocols,such as BlueTooth or 802.11g, or Ethernet. The image data 160 is thenconverted (step 1315) to a desired file format, and header information,and optionally a watermark, may be added to the image data 160. Theheader may contain information regarding the fingerprint 115 and how,when, and where it was acquired, the manufacturer, model and serialnumber of the acquisition device, the unique ID and name of the computeras well as the name of the operator logged on to the operating system,and pointers to related information, such as personal information aboutthe individual, including, but not limited to, a photograph, voicerecording (sometimes referred to as a “voice print”), signature, orother identifying information. A watermark can be added to protect theimage and data header from subsequent unauthorized alteration. Oneexample of a procedure to add a watermark to fingerprint images isdescribed by Yeung and Pankanti, “Verification watermarks on fingerprintrecognition and retrieval” Journal of Electronic Imaging, 9(4), 468-476(2000). Commercial watermarking procedures can also be used.

Continuing to refer to FIG. 13, after the fingerprint is acquired (step1305), specific and unique features of the fingerprint are identified,extracted and stored as a “trained model” (step 1320), according to aprocess described below in reference to FIG. 15. The model may be usedfor subsequent fingerprint verification (one-to-one matching) bycomparing the feature sets of the model to the corresponding features ofa subject, or “present,” image. Features from the entire image areusually extracted and stored for fingerprints that are to be used forfingerprint identification (one-to-many matching). An example process ofconstructing a model is described in reference to FIG. 15.

FIG. 15 is a flow diagram of an example process 1500 for creating amodel of a fingerprint that can be used for subsequent analysis andmatching. At least two models are constructed (step 1505) from highresolution images: a low resolution “outline” model that comprises ridgecontours, and a high resolution “details” model that comprises ridgecontours, ridge shapes, and pores. Details regarding ridge shapes andpores are generally not considered in the outline model; compare FIG. 10for an example of the features that are typically identified at theoutline level to FIGS. 11 and 12 for features found at the detailslevel. A further refinement adds a third, more highly detailed modelspecifically designed to more closely identify pore feature information,such as area, and centroid location. Edge detection levels may bechanged for each of the three models in order to identify specificdetails, such as pores within ridges, with higher accuracy.

Since the outline model contains less information, computation time isreduced by first comparing images at the outline level for a candidatematch before attempting to match candidate images at the details level.Both outline and details models may be constructed using the sameprocedure of gradient edge detection (e.g., gray-scale gradient edgedetection) (step 1515). In one embodiment, the outline model isdetermined from a subset of pixels of the original image; each pixel ofthe subset is an average value over a predetermined number ofneighboring pixels from the original image. The first step duringfingerprint matching is to compare outline models. Matches may bediscovered at the outline level and then compared at the details level.Detailed matches may then be supplemented by required details, and thenfurther to be compared at the fine details pore level.

Images acquired by the fingerprint imager 110 of FIG. 4 are 6.1 mm by7.7 mm, in one embodiment. This relatively large fingerprint is usuallydivided into one or more smaller regions that are individually used toconstruct individual model(s) (step 1510). These model regions caneither be chosen manually to capture particularly interesting featuresfound in a specific region, or they can be chosen automatically usingadjustable software settings that define the number of regions, regionsize, and region locations. Their size is preferably chosen to be largeenough to include numerous characteristic features and small enough toreduce problems associated with plastic deformation of the skin and tominimize computation time by using a smaller relative model size. Thefeatures identified in a sub-region of the complete fingerprint arereferred to as a “feature set.” A trained model comprises a collectionof all of the feature sets for a particular fingerprint, or severaltrained models may each contain feature sets for a portion of theparticular print

Features, for each resolution level and in each region of thefingerprint chosen to be part of the trained model, are identified usinggray-level gradient edge detection procedures (step 1515) and extracted.The gradient is first estimated for each of the pixels of the modelusing one of a relatively large number of procedures that have beendeveloped for this process (see for example D. A. Forsyth, and J. Ponce,“Computer Vision A Modern Approach”, Prentice Hall, New Jersey, 2003,chapter 8).

A particularly useful procedure that is often used to estimate gradientsis to apply a Gaussian noise filter to the image and then to perform thegradient calculation using a “finite differences” algorithm. Aftercalculation of the gradients, an image point with a locally maximalgradient in the direction of the gradient is identified and marked as anedge point. The next step is to identify neighboring edge points. Thisis usually accomplished by finding the nearest pixels to the originaledge point that lie in a direction that is approximately perpendicularto the gradient direction that passes through the first edge point. Thegradient values for these new pixels are determined, and the pixel witha gradient that is (i) maximal along its gradient direction and (ii) hasa value that exceeds a threshold is assigned to be the next edge point.This procedure is continued either until the edge is terminated or theedge closes with itself to form a continuous curve. Edge terminationoccurs at the previously determined edge point if the gradient of acandidate edge point is less than the threshold. In the next step apreviously unvisited edge point is identified and its edge is tracedaccording to the steps outlined above. This whole process is repeateduntil all of the potential edge points have been considered. Automaticsoftware procedures are then used to distinguish fingerprint featuresfrom noise. Real edges, for example, must define features that have aminimum width. In addition, lines that do not enclose pores must extendfor a minimum distance in order to be considered as legitimate featureedges. Further, pores only occur in ridges and not in valleys.Additional rules may be applied by adjusting software settings. Optionalsteps allow the models to be manually edited by adding or deletingfeatures (step 1520) and allow users to indicate certain features of themodel that must be present for a successful match (step 1525). Allediting is performed on the features and not on the original image,which is deliberately protected from any alteration. Examples offeatures that are identified by this procedure were introduced above inreference to FIGS. 10-12. A number of commercial software applicationscan be used for edge detection, or custom software applications may bedesigned for executing the processing described herein. The process 1500returns (step 1530) to the enrollment process 1300 of FIG. 13.

Referring again to FIG. 13, after feature extraction to produce thetrained model, the original fingerprint image and model data 160 isoptionally indexed, compressed and encrypted (step 1325), and storedwith the model (step 1330). One compression procedure is the FederalBureau of Investigation (FBI) standard for 500 dpi fingerprints,referred to as Wavelet/Scalar Quantization (WSQ). This is a lossy schemewith a compression ratio of 12.9. According to the FBI standard, thecompressed image needs to form a record of the original print that canbe used for manual matching and for some automatic matching procedures.Alternatively, a lossless compression technique may be employed forstoring the image. The trained model used for matching, on the otherhand, requires much less storage space and may be stored with or withoutcompression.

Fingerprint matching is used either to verify the identity of anindividual (referred to as 1:1 matching) or to identify the source of anunknown fingerprint (referred to as 1:n search). Both procedures comparea known fingerprint from a database either to a live scan fingerprintfor verification or to an unknown fingerprint for identification. Thisfundamental step of fingerprint matching is the same for bothverification and identification.

Fingerprint verification is a particularly important type of fingerprintmatching. Fingerprint verification compares a live scan image to anenrolled image in order to authenticate the identity of the individualpresenting the live scan fingerprint. Flow diagrams for fingerprintverification are shown in FIGS. 16, 17, 18 and 19.

A user first presents identification (step 1605), such as a passport,license, smartcard or other ID, name or password any of which may beused to provide or to look up his/her previously enrolled fingerprintmodel(s). Note that the models may be looked-up or stored on the ID,depending upon the application. Then, a live scan, high-resolutionfingerprint of the user is acquired in real time (step 1620), and“outline,” “details,” and “pores” features for the complete fingerprintare extracted (step 1625), as illustrated in reference to FIGS. 9-12.

FIG. 16 is a flow diagram of the initial process 1600 for fingerprintverification at an “outline” resolution level. An outline feature setfrom the trained model (steps 1610, 1615) of the user is compared to thecorresponding features from the live scan image (step 1630) according toa process described below in reference to FIG. 18. This comparison isrepeated until either a match is found or all of the feature sets of themodel have been compared to the live scan outline features (steps 1635,1640, 1645). If no match is found, the process 1600 ends (step 1655),and further processing at the details level does not occur for thatparticular model. If a match candidate is found at the outline level(step 1635), the matching procedure is repeated at the details level(process 1650) according to FIG. 17A.

FIG. 17A is a flow diagram of the process 1650 for fingerprintverification at the “details” resolution level. The process 1650 selectsa details feature set from the trained model (step 1705). The process1650 compares the details features from the image and model (step 1710)according to the process of FIG. 18, discussed below. This comparison isrepeated until either a match is found or all of the feature sets of themodel have been compared to the live scan details features (steps 1715,1720, 1725). If no match is found, the process 1650 ends (step 1735),and further processing at the required features level does not occur forthat particular model. If a match candidate is found at the detailslevel (step 1715), and no “required” or fine details “pores” featuresare specified (i.e., details threshold is met), then a positive match isdeclared. If a match candidate is found at the details level (step 1715)and “required” and/or “pores” are specified in the matchingrequirements, the matching procedure is repeated for required features(process 1730) according to FIG. 17B.

FIG. 17B is a flow diagram of the process 1730 for fingerprintverification of [the optional] required features. The process 1730selects required features from a feature set from a trained model (step1732). The process 1730 compares the required features from the featureset of the model to features identified in the live scan image using thesame resolution that was used to obtain the required features of themodel (step 1734), according to the process of FIG. 18, discussed below.The comparison is repeated until either a match is found or all of thefeature sets of the model at the required features level have beencompared to the live scan image (steps 1736, 1738, 1740). If no match isfound, the process 1730 ends (step 1745), and further processing forpore matching does not occur for that particular model. If the requiredfeatures match (step 1736), the matching procedure is repeated for poresat the “fine details” level (if any, process 1750) according to FIG.17C.

FIG. 17C is a flow diagram of the process 1750 for fingerprintverification of the optional pores at the “fine details” resolution. Theprocess 1750 selects pores from a feature set from a trained model atthe fine details resolution (step 1752). The process 1750 compares poresfrom the feature set of the model to pores identified in the live scanimage (step 1754) according to the process of FIG. 18, discussed below.The comparison is repeated until either a match is found or all of thefeature sets of the model at the fine details level have been tested. Ifthe pore features threshold is met, a positive match is declared (step1770).

FIG. 18 is a flow diagram outlining the process used to determine if amatch exists between a trained model and a present image, such as a livescan fingerprint image, which is used now as an example. Fingerprintfeatures are correlated at either the outline, details, or fine detailsresolutions (steps 1630, 1710, 1734, 1754). A model feature set isoverlain on a feature set of the entire live scan image (step 1805).Since the model feature set is generally smaller, it will overlap only aportion of the live scan feature set. A correlation is then calculatedbetween the model feature set and the portion of the live scan imagethat it overlays (step 1810). There are a number of procedures that arecommonly used to determine correlation between images (for example,Maltoni, Maio, Jain, and Prabhakar, “Handbook of FingerprintRecognition”, Springer, 2003, chapter 4). A particularly usefulprocedure considers the similarity of two images to be indicated bytheir “cross correlation” (for example, Maltoni, Maio, Jain, andPrabhakar, “Handbook of Fingerprint Recognition”, Springer, 2003,chapter 4). Using this procedure, the cross correlation between the livescan image and the model feature set is calculated as the feature setexpands, contracts, and rotates, over predetermined limits, with respectto the live scan image that it overlays. The feature set is thentranslated to a new section of the live scan image (step 1825), and thecross correlation calculation is repeated. This process is followed forall the feature sets of the model (step 1820). A match is achieved (step1830) if a cross correlation exceeds a threshold (step 1815), and if apredetermined percent of features match with the live scan imagefeatures (step 1830). Otherwise, there is no match (step 1840).

FIG. 19 is an “adaptive conformity” process 1900 optionally executed indetermining the correlation (step 1810). The adaptive conformity process1900 is used to improve performance of the correlation process of FIG.18. Adaptive conformity allows for matching of fingerprint features incases where linear or non-linear shifting of the fingerprint featuresoccurs either in the original fingerprint from which an outline, and/ordetails, and/or fine details level model(s) is derived, or in imaging alive scan fingerprint image. Such linear or non-linear shifting iscaused by elasticity of the skin in combination with pressing the finger105 into the imaging surface (e.g., lens 415) in various ways, such asthrough applying pressure in latitudinal, longitudinal, or rotationaldirections or in an excessively hard or light manner. In thelatitudinal, longitudinal, or rotational cases, part of the fingerprintmay be imaged normally, and compression, extension, or rotation mayoccur in another part of the fingerprint. In the excessively hard orlight pressure cases, part or all of the fingerprints may includethicker or thinner ridge 905 sizes, respectively. The adaptiveconformity process 1900 allows the shape of the model to adaptivelyconform to account for non-uniformity of fingerprinting but in a mannerthat does not alter or change the features in any way, therebymaintaining the integrity to the identification and verificationprocesses according to the principles of the present invention. Similarshapes, details, and pores that are shifted slightly due to distortionare recognized as the same shapes, but in a slightly different relativeposition than they were on a previous acquisition. The principles of thepresent invention allow for highly accurate recognition of level threedetails even if distortion from previous enrollment has taken place,which is normal to some degree in any set of fingerprint images. Inaddition, better accuracy to match is obtained with slight changes dueto wear of ridges, scarring, dehydration, and pressure.

Referring specifically to FIG. 19, the adaptive conformity process 1900may be executed for authentication or identification (i.e.,verification) processes, and with any image source including but notlimited to latent images. After starting (step 1905), the process 1900returns to the fingerprint feature correlation process 1800 of FIG. 18if adaptive conformity is not selected to be applied by the user (step1920). In one embodiment, the user may select adaptive conformity to beselected through toggling a Graphical User Interface (GUI) control. Ifadaptive conformity is selected, the process 1900 identifies a candidatematch start location (step 1915), such as the core (i.e., center offingerprint “swirls”), and uses the start location as a “base” fromwhich to apply the adaptive conformity linear or non-linear processing.

After identifying the start location, the process 1900 locates thepixels where edge topology (i.e., outline level) features of the modeland the image deviate. Using the deviation point as a “base point ofadaptive conformity,” the process 1900 attempts to conform potentiallycorresponding model pixels beyond the base point laterally,longitudinally, or radially (step 1930) with the fingerprint features ofthe live scan fingerprint image. Conforming the potentiallycorresponding model pixels means to shift the model pixels in apredetermined direction without changing the shape of the outline ordetails features of the model. If the chosen predetermined direction iscorrect, the shifting of the edge topology continues by shifting pixelsin the same direction from the base deviation while the shape of theedge topology continues to match (steps 1935, 1940, and 1945) or untilthe edge topology being examined is complete. If the chosenpredetermined direction is incorrect, other directions may be tried.Distance ranges for allowing pixels to move in attempting to adaptivelyconform can be specified by the user but are limited in range to thattypical of these types of distortions.

Additional testing at the outline level continues (step 1950) until theoutline feature sets of the model are compared with the outline featuresof the live scan image (steps 1950 and 1925-1945). Following comparisonof the outline features, the process 1900 repeats the adaptiveconformity process at the details level (step 1955). The process 1900returns to the process of FIG. 18 at step 1810 following completion(step 1960) at the details level. In an alternative embodiment, if thematching at the outlines level does not achieve a predeterminedthreshold, the processing at the details level (step 1955) is skippedwith an appropriate message being passed to the process of FIG. 8.

Fingerprints from an unknown individual can sometimes be identified bycomparing the unknown print to a database of known prints; this isreferred to as 1:n fingerprint identification. A particularlyadvantageous procedure is to compare fingerprints in a database of highresolution fingerprints that were acquired and processed according tothe processes of FIGS. 13-15 with a high resolution unknown fingerprint.In this case, each fingerprint in the database has an associated“outlines” and “details” and optionally, a “required” and “pores”feature set that encompasses the entire fingerprint image. A trainedmodel is formed from the unknown fingerprint and compared to the featuresets of the database. To save computation time, the comparison is firstmade at the “outline” resolution level. The subset of candidatefingerprints that match at the outline resolution level is subsequentlycompared at the “details,” and optionally at “required” or “pore”resolution(s).

It is also possible to compare a relatively low resolution unknown printto prints in either a high or a low resolution database. In these cases,the feature set for the unknown fingerprint includes only ridge patternsand does not contain information on ridge profiles, ridge shapes, orpores. The processes described herein exhibit enhanced reliability overminutiae-based systems even in this case since all of the information ofthe fingerprint is used for comparison and not just a few minutiaepoints.

In most examples of fingerprint identification, appropriate linearscaling might be necessary since the unknown fingerprint may have beenacquired at a different magnification from the fingerprints in thecomparison database. The GUI 620 allows a user to graphically mark aline having start and end points of a scale (e.g., ruler) imaged withthe original fingerprint and assign a length value to the length of theline. In this way, proper scaling can be applied for comparison againsta live scan image, for example.

Similarly, appropriate angular rotation might be necessary since theunknown fingerprint may have been acquired at a different angle than thefingerprints in the comparison database. The GUI allows a user toindicate a range of rotation (e.g., + or −30 degrees) to check the livescan image against the outline level of the comparison model. In thisway, proper orientation can be obtained for the comparison, andsubsequent details, required, and pore features can then be applied atthe same rotation, for example. Checking a larger degree of scale and/orrotation takes more computing time, so faster compare times are achievedif some degree of normalization is first preprocessed in order to limitthe degree of scaling and/or rotation that is required for reliableoperation of the system.

In addition to the processing described above, the principles of thepresent invention support preprocessing that can improve performance ofthe processing (i.e., modeling and comparisons). Example forms ofpreprocessing include: image orientation and rotation, sub-sampling,decimating, binning, flattening the field, accounting for defectivepixels in the sensor array 215, encrypting the image data 160, applyinga watermark, and attaching sensor information.

FIGS. 20A and 20B are illustrations of one form of preprocessing, namelyaccounting for defective pixels. Referring first to FIG. 20A, a grid2000 illustrates an example of a small region of pixels in the sensorarray 215. The processing region of interest 2005 starts at a firstposition. In this embodiment, the processing region of interest iscomposed of an array of 3×3 pixels. Alternative embodiments may utilizealternative array sizes (5×5, 9×9, etc) or alternative array shapes(rectangular) ultimately determined by the desired resolution of thefingerprint sensor and the desired image quality. In the presentexample, there are multiple bad pixels in the sensor array 215, eachrepresented by a zero ‘0’. Among the pixels, there are known good pixelsrepresented by a one ‘1’. In the center of the processing region ofinterest 2005 is a pixel of interest, represented by a plus sign ‘+’. Inthis example, the pixel of interest, if a defective pixel, may becorrected by obtaining an average of at least two neighboring pixelswithin the processing region of interest 2005. The average value of theneighboring pixels can then be assigned to the defective pixel ofinterest. Once a pixel of interest has been corrected (if necessary),the processing region of interest 2005 is moved by one pixel spacing inorder to address the next pixel in the array. The direction of movementof the processing region of interest 2005 may be in the most convenientdirection for processing efficiency or dictated by the architecture ofthe particular system.

FIG. 20B is an example of the shifting of the processing region ofinterest 2005 to a neighboring pixel on the pixel array 2000. Notingthat the pixel of interest previously corrected in FIG. 20A is nowdenoted as a ‘C’ pixel in FIG. 20B, the processing region of interestcorrects the next pixel of interest (also denoted by a + in FIG. 20B) byaveraging two known good pixels from within the processing region ofinterest 2005. In alternative embodiments, previously corrected pixels(denoted as ‘C’ in FIG. 20B) may be used as a good pixel for thepurposes of correcting the new, defective pixel of interest.

Although this embodiment averages the intensities of two known goodpixels within each processing region of interest to correct for adefective pixel, alternative embodiments may average the intensities ofmore than two known good pixels. In yet another embodiment, a defectivepixel of interest may be replaced by intensity data from one known goodpixel from within the processing region of interest 2005, wherein theprocessing region of interest 2005 may be of an array size larger than3×3 or the processing region of interest array size may not be a squaretwo-dimensional array.

The other examples of preprocessing are now described without referenceto associated figures.

Subsampling is a form of reducing the amount of data sampled by thefingerprint camera 120. Instead of reading data from every pixel of thesensor array 215, the camera 120 reads a subset of the pixels in apredefined manner, which is preferably selectable by the user.Decimating is another form of reducing the data, but reduces the amountof data after the data has been read from all or substantially all thepixels of the sensor array 215. Binning is a technique for reducing theamount of data representing the fingerprint by averaging data ofmultiple pixels into a single value; for example, four pixels may beaveraged as a single average value to reduce the data by a factor offour.

Flattening the field is also referred to as correcting for unevenillumination of the fingerprint image. Uneven illumination of the imagemay be caused by many sources, such as the light source 205, thepropagation path of the light beam 207, the optics 210 (including theoptical elements 250 or HOE 255), or the gain or offset of the pixels ofthe sensor array 215. Testing for uneven imaging of the fingerprint maybe done during a calibration phase prior to acquiring an image of afingerprint, where a reflection off the fingerprint imaging surface isused as the source and calibration data is determined and stored in themicroprocessor memory 355 (FIG. 3B), for example. The calibration datais applied during live scanning of a fingerprint 115.

In addition to reducing data or correcting for imaging or equipmentissues, the preprocessing may also be used to apply additionalinformation to the image data 160. For example, the image data 160 maybe encrypted using various forms of well-known encrypting techniques. Aheader may be applied to add a variety of equipment-related informationor other information that may be useful to determine the source of theequipment or understand the environmental conditions that were presentwhen the fingerprint was imaged for use at a later time, such as duringauthentication or authorization. Example information may be themanufacturer, model and serial number of the instrument supplying theimage data 160 representing the portion of the fingerprint, date offingerprinting, time of fingerprinting, calibration data associated withthe instrument used to acquire the image, the name of the operatorlogged onto the operating system, the unique ID of the computer that wasused to acquire the image, and temperature at the time the image wasacquired.

A watermark may also be associated with the data. An example techniquefor applying a watermark is to use extra data bits associated with eachpixel to represent a portion of the watermark. For example, if each datapixel only represents two hundred fifty-six (2⁸) levels of gray-scale,two bits of a ten-bit word may be used to represent a portion of awatermark. The watermark may be information that would otherwise be partof a header or may be information that is used to determine whether anytampering has occurred with the fingerprint image.

The processing and preprocessing discussed herein may be implemented inhardware, firmware, or software. In the case of software, the softwaremay be stored locally with a processor adapted to execute the softwareor stored remotely from the processor and downloaded via a wired orwireless network. The software may be stored in RAM, ROM, optical disk,magnetic disk, or other form of computer readable media. A processorused to execute the software may be a general purpose processor orcustom designed processor. The executing processor may use supportingcircuitry to load and execute the software.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

For example, in FIG. 1, the fingerprint sensor 100 and local computer130 are distinct devices with a majority of the processing occurring inthe local computer 130. In an alternative embodiment, the fingerprintsensor 100 includes an embedded processor capable of performingpreprocessing as well as some of the processing functions of the localcomputer 130, as discussed in general in reference to FIGS. 6A and 6Band in detail in reference to FIGS. 13-20. Also, the processing may bedone in the remote computer 150.

The control channel/data link 125 or other links 132, 152, 145 may bewired or wireless links, such as through Radio Frequency (RF) orinfrared communications. In another embodiment, the fingerprint sensor100 may have IEEE 802.11, cellular communications (e.g., Code DivisionMultiple Access (CDMA)), or other wireless capability to interfacedirectly to a wireless node (e.g., base station, not shown) in thecomputer network 140.

The local and remote databases 135, 155 may be any form of database andlocated with or distinct from the associated computers or distributedabout the computer network 100. There may also be security provisionsassociated with the databases 135, 155 so as to prevent tampering withthe fingerprint image data and models 170 stored therein.

In FIGS. 3A and 3B, real-time automatic feedback control of power,angle, or wavelength is illustrated. In alternative embodiments,mechanisms to allow for manual adjustments may be provided. Also,periodic, random, or calibration period adjustments instead of real-timefeedback control may be employed to reduce power consumption. The powersource for the fingerprint sensor 100 is described above as being thelocal computer 130 via Firewire or other interface; however, it shouldbe understood that the power source may be a battery (not shown) orAC-to-DC power converter with sufficient filtering circuitry to achievethe high-resolution images. Also, the sensor array 215 may be a CCDarray or any other array adapted for use in the fingerprint imagingapplication for achieving resolution sufficient to detect thefingerprint features described herein.

In FIGS. 6A and 6B, the software 610 may be any language adapted to beexecuted in the fingerprint sensor 100, local computer 130, or remotecomputer 150. For example, the software may be assembly language, ‘C’,object-oriented C++, Java, or combinations thereof. It should beunderstood that the processing and display of live scan images should bedisplayed in near-real-time, preferably with outline or details modelsdisplayed thereon.

FIG. 7 may omit the preprocessing 710 and processing 715 of the imagedata 160. Instead, the image data 160 may be stored in one of thedatabases 135, 155, for example, and processed according to thepreprocessing 710 or processing 715 techniques as described herein in apost-processing, non-real-time manner. Additionally, in anotherembodiment, after images are captured by the fingerprint sensor 100 andindexed and stored, certain software-based enhancements may beperformed, if needed, at the option of a system configurationadministrator or for other beneficial reasons. Certain enhancements thatdo not alter the original image or its unique characteristics can beperformed to enhance image analysis, such as mean low-pass filtering orautomatic level adjustment to improve contrast or other methods thatinclude, but are not limited to, gray-scale gradient edge detectiontechniques for pattern recognition and subsequent matching techniques,or combinations thereof.

A number of commercial software applications can be used for edgedetection, including Aphelion from Amerinex Applied Imaging, Hexsightsoftware from Adept, Vision Blox distributed in the U.S.A. by ImageLabs, and Halion from The Imaging Source.

FIG. 8 illustrates a gradient edge detector 805 and modeler 810 asdistinct operational units. In alternative embodiments, these twooperational units 805, 810 may be combined into an integratedoperational unit or distributed and executed about multiple processors.Further, the image data and model(s) 170 may be stored in the localdatabase 135, as shown, or stored in different databases for size,security, or administrative reasons.

FIGS. 9-12 are fingerprint images 900 embodied in high-resolution imagedata 160 representing the fingerprint 115. Because the processingdescribed herein is robust, the image data 160 may be lower resolutionthan illustrated and still achieve the desired matching results. Inaddition, the gradient edge detection processes described herein canaccomplish the modeling and comparing processes at poor contrast levelswithout significant degradation to the results. However, the user maychoose to lower a matching threshold at either outline or details levelsto account for poor contrast levels in the original fingerprint image.

FIGS. 13-20 are illustrative flow diagrams. The flow diagrams may bevaried in any manner that accomplishes the processing and storage taskssuitable for achieving the acquisition, modeling, and verificationaspects according to the principles of the present invention.

What is claimed is:
 1. A method for processing an image of a biometriccomprising: applying a gradient edge detection process, using a gradientedge detector, to detect structural features in a biometric based ondata representing an image of at least a portion of the biometric; andforming a model, using a modeler, of the image as a function of thedetected structural features wherein forming a model of the imageincludes modeling at least one region of the image of the at least oneportion of the biometric.
 2. The method according to claim 1 whereinforming a model of the image includes constructing the model for atleast two resolutions.
 3. The method according to claim 2 wherein theresolutions include an outline model at a low resolution and a detailsmodel at a high resolution.
 4. The method according to claim 3 whereinthe biometric is an area of skin with a friction ridge pattern, theoutline model includes edge topology of ridge features, and the detailsmodel includes edge topology and specifics of ridge deviations andlocations and sizes of pores.
 5. The method according to claim 2 whereinthe resolutions include an outline model at a low resolution, a detailsmodel at a high resolution, and a fine details model used to locate anddefine particular biometric features more accurately than at the low orhigh resolutions.
 6. The method according to claim 2 wherein thebiometric is an area of skin with a friction ridge pattern andconstructing the model includes identifying, outlining, and extractingridge deviation detail and pore features.
 7. The method according toclaim 6 wherein the ridge deviation detail includes ridge contoursincluding scars; and the pore features include position, shape, andsizes of pores.
 8. The method according to claim 2 wherein the biometricincludes at least one of the following: ear shape and structure, facialor hand thermograms, iris or retina structure, handwriting,fingerprints, palm prints, foot prints, toe prints.
 9. The methodaccording to claim 1 wherein a gradient is estimated for each pixel ofthe model after applying a noise filter to the image.
 10. The methodaccording to claim 1 wherein the detection process includes using afinite differences process.
 11. The method according to claim 1 whereinthe detection process includes: after calculating the gradients,identifying and marking an image point as an edge point having a locallymaximal gradient in the direction of the gradient that exceeds athreshold; identifying neighboring edge points by finding nearest pixelsto the original edge point that lie in a direction that is approximatelyperpendicular to the gradient direction that passes through the firstedge point; for the nearest pixels, determining gradient values and, forthe pixel with a gradient that is maximal along its gradient directionand has a value that exceeds a threshold, assigning the pixel to be thenext edge point; continuing either until the edge is terminated or theedge closes with itself to form a continuous curve; terminating theprocess at the previously determined edge point if the gradient of thecandidate edge point is less than the threshold; and repeating theprocess until all potential edge points have been considered.
 12. Themethod according to claim 1 further including automaticallydistinguishing biometric features from noise.
 13. The method accordingto claim 12 further including characterizing the noise as a biometricfeature that is below a minimum width or extends less than a minimumdistance.
 14. The method according to claim 1 further includingsupporting manual editing of features and selecting features that mustbe present for a successful match.
 15. The method according to claim 1wherein forming a model of the image includes overlapping portions ofmultiple regions.
 16. The method according to claim 1 wherein forming amodel of the image includes allowing a user to add, extend, or deletefeatures.
 17. The method according to claim 1 wherein forming a model ofthe image includes allowing a user to identify features that must bepresent for a match.
 18. The method according to claim 1 wherein forminga model of the image includes allowing the user to adjust the size orposition of the model relative to the biometric.
 19. The methodaccording to claim 1 wherein the image is a previously stored image; andforming a model of the image includes normalizing a size of the image asa function of a scale associated with the image.
 20. The methodaccording to claim 1 wherein the biometric is an area of skin with afriction ridge pattern and the features include ridge structure withridge deviation detail.
 21. The method according to claim 1 furtherincluding displaying the image to a user with an overlay of indicationsof the biometric features of the image.
 22. The method according toclaim 1 further including displaying the image to a user with an overlayof indications of filtered biometric features according to a selectablecriteria.
 23. The method according to claim 1 further includingautomatically rotating the image to a specified orientation fordisplaying to a user.
 24. The method according to claim 1 wherein theimage is a gray-scale image.
 25. The method according to claim 1 furtherincluding adding the image of the at least a portion of the biometric toa database.
 26. The method according to claim 25 wherein adding theimage includes storing the image and the model of the image in thedatabase.
 27. The method according to claim 25 wherein adding the imageincludes storing the image at full sampled resolution.
 28. The methodaccording to claim 25 wherein adding the image includes compressing thedata representing the image prior to storing in the database.
 29. Themethod according to claim 28 wherein compressing the image includescompressing the image in a lossless manner.
 30. The method according toclaim 25 wherein adding the image includes compressing the model priorto storing the image in the database.
 31. The method according to claim25 wherein adding the image includes encrypting the data or model priorto storing the image in the database.
 32. The method according to claim25 wherein adding the image includes storing the image and the model ofthe at least a portion of the biometric with associated information. 33.The method according to claim 32 wherein the associated informationincludes at least one of: identity of a person associated with thebiometric; manufacturer, model, or serial number of the instrumentsupplying the data representing the portion of the biometric; date ofbiometric imaging; time of day of biometric imaging; calibration dataassociated with the instrument used to acquire the image; temperature atthe time the image was acquired; unique computer ID receiving the datarepresenting the image from the instrument acquiring the image of thebiometric; or name of person logged into the computer at the time theimage was acquired.
 34. The method according to claim 32 wherein theassociated information includes a photograph, voice recording, orsignature of the person whose biometric is imaged.
 35. The methodaccording to claim 32 wherein the associated information is a watermark.36. The method according to claim 35 wherein the watermark isidentifying information.
 37. The method according to claim 35 whereinthe watermark includes anti-tampering information.
 38. The methodaccording to claim 1 further including comparing a previously storedmodel from a database to a present image.
 39. The method according toclaim 38 wherein the present image is at least a portion of a biometricof a person having a known identity.
 40. The method according to claim38 wherein the present image is at least a portion of a biometric of aperson having an unknown identity.
 41. The method according to claim 38wherein the present image is received from one of the following sources:live source, local database, scanned image, or other source.
 42. Themethod according to claim 38 wherein comparing includes comparingoutline features of the previously stored model to outline features ofthe present image to determine (i) whether the present image is acandidate for a match or (ii) whether the previously stored model is acandidate for a match.
 43. The method according to claim 42 whereincomparing includes determining whether the comparison exceeds apredetermined candidate threshold.
 44. The method according to claim 42wherein, if the present image is not a candidate for a match, comparingincludes comparing outline features of another previously stored modelto the outline features of the present image to determine whether thepresent image is a candidate for a match and if so, using the nextpreviously stored model for details comparison.
 45. The method accordingto claim 42 wherein, if the previously stored model is not a candidatefor a match, comparing includes comparing outline features of a nextpreviously stored model to the outline features of the present image todetermine whether the next previously stored model is a candidate for amatch and if so, using the next previously stored model for detailscomparison.
 46. The method according to claim 42 wherein, if a candidatematch of outline features is found, comparing includes comparing detailsfeatures of the previously stored model with details features of thepresent image.
 47. The method according to claim 46 wherein comparingdetails features includes determining whether the details comparisonexceeds a predetermined threshold.
 48. The method according to claim 46wherein comparing details features includes determining whether requiredfeatures associated with the previously stored model are found in thepresent image.
 49. The method according to claim 46 wherein thebiometric is an area of skin with a friction ridge pattern and whereincomparing includes comparing fine details features, wherein comparingfine details features includes determining whether pore features in thepreviously stored model are found in the present image.
 50. The methodaccording to claim 49 further including indicating which pores in thepreviously stored model appear in expected locations in the presentimage, including allowing for distortions that normally occur betweensuccessive impressions.
 51. The method according to claim 50 furtherindicating a pore count or a statistical probability of an error in atleast cases allowing for distortions.
 52. The method according to claim46 wherein comparing details features includes determining whether thedetails comparison exceeds a predetermined threshold a specified numberof consecutive frames.
 53. The method according to claim 52 wherein thedetails features in successive frames are different.
 54. The methodaccording to claim 52 further including selecting another detailsfeature set of the previously stored model for correlating with anotherdetails feature set of the present image.
 55. The method according toclaim 52 further including: (i) selecting another previously storedmodel for correlating with a features set of the present image and (ii)declaring a successful match if any model exceeds a predeterminedthreshold.
 56. The method according to claim 38 wherein comparingincludes comparing outline features of the previously stored model tooutline features of a model of the present image and, if the comparisonexceeds a predetermined threshold, comparing details features of thepreviously stored model to details features of the model of the presentimage to determine whether the previously stored model and the presentimage match.
 57. The method according to claim 38 wherein comparingincludes scaling the previously stored model, present image, or model ofthe present image.
 58. The method according to claim 38 whereincomparing includes rotating the previously stored model, present image,or present model.
 59. The method according to claim 38 wherein comparingincludes adaptively conforming the previously stored model to accountfor variability associated with recording or acquiring the presentimage.
 60. The method according to claim 59 wherein accounting forvariability includes accounting for an expected location of predefinedfeatures.
 61. The method according to claim 59 wherein the variabilityincludes stretching of the biometric or portions thereof laterally,longitudinally, or radially.
 62. The method according to claim 59wherein the variability is caused by pressure of the biometric on amedium used to record or acquire the present image.
 63. The methodaccording to claim 38 wherein comparing includes comparing thepreviously stored model against multiple present images until a match isfound or comparison with the multiple present images is complete. 64.The method according to claim 38 wherein comparing includes comparingmultiple previously stored models against the present image until amatch is found or comparison with the multiple previously stored modelsis complete.
 65. The method according to claim 38 wherein comparingincludes comparing multiple previously stored models against multiplepresent images until comparing against the multiple previously storedmodels is complete.
 66. The method according to claim 38 wherein thepresent image includes multiple biometrics of an individual.
 67. Themethod according to claim 38 wherein the multiple biometrics aredifferent areas of skin with a friction ridge pattern from the sameindividual.
 68. The method according to claim 1 further includingcomparing previously stored models of multiple biometrics to presentimages of multiple, respective biometrics.
 69. The method according toclaim 68 further including determining a combined metric based on thecomparisons.
 70. The method according to claim 1 further includingpreprocessing the data representing the image.
 71. The method accordingto claim 70 further including subsampling the at least a portion of thebiometric to produce the data representing the image.
 72. The methodaccording to claim 70 wherein preprocessing includes decimating the datarepresenting the image.
 73. The method according to claim 70 whereinpreprocessing includes binning the data representing the image.
 74. Themethod according to claim 70 wherein preprocessing includes correctingfor uneven imaging of the at least a portion of the biometric.
 75. Themethod according to claim 70 wherein preprocessing includes accountingfor defective pixels of an instrument used to acquire the at least aportion of the biometrics.
 76. The method according to claim 70 whereinpreprocessing includes encrypting the data representing the image. 77.The method according to claim 70 wherein preprocessing includes changingthe image orientation, including changing the image orientation byflipping the image vertically or horizontally or by rotating the image.78. The method according to claim 70 wherein preprocessing includesattaching sensor information to the data representing the image.
 79. Themethod according to claim 70 wherein preprocessing includes applying awatermark to the data representing the image.
 80. The method accordingto claim 79 wherein the watermark includes information used fortamper-proofing the image to allow for identifying a modified image ormodified information associated with the image.
 81. An apparatus forprocessing an image of a biometric, comprising: a gradient edge detectorconfigured to detect structural features in a biometric based on datarepresenting an image of at least a portion of the biometric; and amodeler configured to form a model of the image as a function of thedetected structural features wherein the modeler is configured to modelat least one region of the image of the at least one portion of thebiometric.
 82. The apparatus according to claim 81 wherein the modeleris configured to construct the model for at least two resolutions. 83.The apparatus according to claim 82 wherein the resolutions include anoutline model at a low resolution and a details model at a highresolution
 84. The apparatus according to claim 83 wherein the biometricis an area of skin, with a friction ridge pattern and the outline modelincludes edge topology of ridge features, and the details model includesedge topology and specifics of ridge deviations and locations and sizesof pores.
 85. The apparatus according to claim 82 wherein theresolutions include an outline model at a low resolution, a detailsmodel at a high resolution, and a fine details model used to locate anddefine particular biometric features more accurately than at the low orhigh resolutions.
 86. The apparatus according to claim 82 wherein thebiometric is an area of skin with a friction ridge pattern and themodeler includes an identifier, outliner, and extractor to determineridge deviation detail and pore features.
 87. The apparatus according toclaim 86 wherein the ridge deviation detail includes ridge contoursincluding scars; and the pore features include position, shape, andsizes of pores.
 88. The apparatus according to claim 82 wherein thebiometric includes at least one of the following: ear shape andstructure, facial or hand thermograms, iris or retina structure,handwriting, fingerprints, palm prints, or toe prints.
 89. The apparatusaccording to claim 81 wherein a gradient is estimated for each pixel ofthe model after applying a noise filter to the image.
 90. The apparatusaccording to claim 81 wherein the detector includes a finite differencesprocessor.
 91. The apparatus according to claim 81 wherein the detectoris configured to: after gradients have been calculated, identify andmark an image point as an edge point having a locally maximal gradientin the direction of the gradient that exceeds a threshold value;identify neighboring edge points by finding nearest pixels to theoriginal edge point that lie in a direction that is approximatelyperpendicular to the gradient direction that passes through the firstedge point; for the nearest pixels, determine gradient values and, forthe pixel with a gradient that is maximal along its gradient directionand has a value that exceeds a threshold, assign the pixel to be thenext edge point; continue either until the edge is terminated or theedge closes with itself to form a continuous curve; terminate theprocess at the previously determined edge point if the gradient of thecandidate edge point is less than the threshold; and repeat the processuntil all potential edge points have been considered.
 92. The apparatusaccording to claim 81 wherein the detector is configured toautomatically distinguish biometric features from noise.
 93. Theapparatus according to claim 92 wherein the detector is configured tocharacterize noise as a biometric feature that is below a minimum widthor extends less than a minimum distance.
 94. The apparatus according toclaim 81 further including a manual editing mechanism in communicationwith the detector to support manual editing of features and selecting offeatures that must be present for a successful match.
 95. The apparatusaccording to claim 81 wherein the modeler is configured to modeloverlapping portions of multiple regions.
 96. The apparatus according toclaim 81 wherein the modeler is configured to allow a user to add,extend, or delete features.
 97. The apparatus according to claim 81wherein the modeler is configured to allow a user to identify featuresthat must be present for a match.
 98. The apparatus according to claim81 wherein the modeler is configured to allow the user to adjust thesize or position of the model relative to the biometric.
 99. Theapparatus according to claim 81 wherein the image is a previously storedimage; and the modeler is configured to normalize a size of the image asa function of a scale associated with the image.
 100. The apparatusaccording to claim 81 wherein the biometric is an area of skin with afriction ridge pattern and the features include ridge structure withridge deviation detail.
 101. The apparatus according to claim 81 furtherincluding a display in communication with the modeler configured todisplay the image to a user with an overlay of indications of thebiometric features of the image.
 102. The apparatus according to claim81 further including a display in communication with the modelerconfigured to display the image to a user with an overlay of indicationsof filtered biometric features according to a selectable criteria. 103.The apparatus according to claim 81 further including an imagemanipulator configured to automatically rotate the image to a specifiedorientation for displaying to a user.
 104. The apparatus according toclaim 81 wherein the image is a gray-scale image.
 105. The apparatusaccording to claim 81 further including a database that stores the imageof the at least a portion of the biometric.
 106. The apparatus accordingto claim 105 wherein the database stores the image and the model of theimage.
 107. The apparatus according to claim 105 wherein the databasestores the image at full sampled resolution.
 108. The apparatusaccording to claim 105 further including a compressor that compressesthe data representing the image prior to storing in the database. 109.The apparatus according to claim 108 wherein the compressor compressesthe image in a lossless manner.
 110. The apparatus according to claim105 wherein the compressor compresses the model prior to storing theimage in the database.
 111. The apparatus according to claim 105 furtherincluding an encryption unit that encrypts the data or model prior tostoring the image in the database.
 112. The apparatus according to claim105 wherein the database stores the image and the model of the at leasta portion of the biometric with associated information.
 113. Theapparatus according to claim 112 wherein the associated informationincludes at least one of: identity of a person associated with thebiometric; manufacturer, model, or serial number of the instrumentsupplying the data representing the portion of the biometric; date ofimaging the biometric; time of day of imaging the biometric; calibrationdata associated with the instrument used to acquire the image;temperature at the time of acquiring the image; photograph, voicerecording, or signature of the person whose biometric is imaged;watermark; unique computer ID of the computer receiving the datarepresenting the image from the instrument acquiring the image of thebiometric; or name of person logged into the computer at the time ofacquiring the image.
 114. The apparatus according to claim 112 whereinthe associated information includes a photograph, voice recording, orsignature of the person whose biometric is imaged.
 115. The apparatusaccording to claim 112 wherein the associated information is awatermark.
 116. The apparatus according to claim 115 wherein thewatermark is identifying information.
 117. The apparatus according toclaim 115 wherein the watermark includes anti-tampering information.118. The apparatus according to claim 81 further including an input unitthat provides a present image, a database that stores previouslyacquired images and associated models, and a comparison unit thatcompares a previously stored model from a database to a present image.119. The apparatus according to claim 118 wherein the present image isat least a portion of a biometric of a person having a known identity.120. The apparatus according to claim 118 wherein the present image isat least a portion of a biometric of a person having an unknownidentity.
 121. The apparatus according to claim 118 wherein the presentimage is received from one of the following sources: live source, localdatabase, image scanner, or other source.
 122. The apparatus accordingto claim 118 wherein the comparison unit compares outline features ofthe previously stored model to outline features of the present image todetermine (i) whether the present image is a candidate for a match or(ii) whether the previously stored model is a candidate for a match.123. The apparatus according to claim 122 wherein the comparison unitdetermines whether the comparison exceeds a predetermined candidatethreshold.
 124. The apparatus according to claim 122 wherein, if thepresent image is not a candidate for a match, the comparison unitcompares outline features of a next previously stored model to theoutline features of the present image to determine whether the presentimage is a candidate for a match and if so, uses the next previouslystored model for details comparison.
 125. The apparatus according toclaim 122 wherein, if the previously stored model is not a candidate fora match, the comparison unit compares outline features of a nextpreviously stored model to the outline features of the present image todetermine whether the next previously stored model is a candidate for amatch and if so, uses the next previously stored model for detailscomparison.
 126. The apparatus according to claim 122 wherein, if acandidate match of outline features is found, the comparison unitcompares details features of the previously stored model with detailsfeatures of the present image.
 127. The apparatus according to claim 126wherein the comparison unit further determines whether the detailscomparison exceeds a predetermined threshold.
 128. The apparatusaccording to claim 126 wherein the comparison unit further determineswhether required features associated with the previously stored modelare found in the present image.
 129. The apparatus according to claim126 wherein the biometric is an area of skin with a friction ridgepattern and the comparison unit compares details features, wherein thecomparison unit determines whether pore features in the previouslystored model are found in the present image.
 130. The apparatusaccording to claim 129 wherein the detector indicates which pores in thepreviously stored model appear in expected locations in the presentimage, the detector allowing for distortions that normally occur betweensuccessive impressions.
 131. The apparatus according to claim 130wherein the detector further indicates a pore count or a statisticalprobability of an error in at least cases allowing for distortion. 132.The apparatus according to claim 126 wherein the comparison unit furtherdetermines whether the details comparison exceeds a predeterminedthreshold a specified number of consecutive frames.
 133. The apparatusaccording to claim 132 wherein the details features in successive framesare different.
 134. The apparatus according to claim 132 wherein thecomparison unit selects another details feature set of the previouslystored model for correlating with another details feature set of thepresent image.
 135. The apparatus according to claim 132 wherein thecomparison unit (i) selects another previously stored model forcorrelating with a feature set of the present image and (ii) declares asuccessful match if any model exceeds a predetermined threshold. 136.The apparatus according to claim 118 wherein the comparison unitcompares outline features of the previously stored model to outlinefeatures of a model of the present image and, if the comparison exceedsa predetermined threshold, compares details features of the previouslystored model to details features of the model of the present image todetermine whether the previously stored model and the present imagematch.
 137. The apparatus according to claim 118 wherein the comparisonunit scales the previously stored model, present image, or model of thepresent image.
 138. The apparatus according to claim 118 wherein thecomparison unit rotates the previously stored model, present image, orpresent model.
 139. The apparatus according to claim 118 wherein thecomparison unit adaptively conforms the previously stored model toaccount for variability associated with recording or acquiring thepresent image.
 140. The apparatus according to claim 139 wherein thecomparison unit accounts for variability by accounting for an expectedlocation of predefined features.
 141. The apparatus according to claim139 wherein the variability includes stretching of the biometric orportions thereof laterally, longitudinally, or radially.
 142. Theapparatus according to claim 139 wherein the variability is caused bypressure of the biometric on a medium used to record or acquire thepresent image.
 143. The apparatus according to claim 118 wherein thecomparison unit compares the previously stored model against multiplepresent images until a match is found or comparison with the multiplepresent images is complete.
 144. The apparatus according to claim 118wherein the comparison unit compares multiple previously stored modelsagainst the present image until a match is found or comparison with themultiple previously stored models is complete.
 145. The apparatusaccording to claim 118 wherein the comparison unit compares multiplepreviously stored models against multiple present images until comparingthe multiple previously stored models is complete.
 146. The apparatusaccording to claim 118 wherein the present image includes multiple areasof skin with a friction ridge pattern of an individual.
 147. Theapparatus according to claim 118 wherein the multiple biometrics aredifferent areas of skin with a friction ridge pattern from the sameindividual.
 148. The apparatus according to claim 81 further including acomparison unit that compares previously stored models of multiplebiometrics to present images of multiple, respective biometrics. 149.The apparatus according to claim 148 wherein the comparison unit furtherdetermines a combined metric based on the comparisons.
 150. Theapparatus according to claim 81 further including a preprocessor incommunication with the detector that preprocesses the data representingthe image.
 151. The apparatus according to claim 150 wherein thepreprocessor subsamples the at least a portion of the biometric toproduce the data representing the image.
 152. The apparatus according toclaim 150 wherein the preprocessor decimates the data representing theimage.
 153. The apparatus according to claim 150 wherein thepreprocessor bins the data representing the image.
 154. The apparatusaccording to claim 150 wherein the preprocessor corrects for unevenimaging of the at least a portion of the biometric.
 155. The apparatusaccording to claim 150 wherein the preprocessor accounts for defectivepixels of an instrument used to acquire the at least a portion of thebiometric.
 156. The apparatus according to claim 150 wherein thepreprocessor encrypts the data representing the image.
 157. Theapparatus according to claim 150 wherein the preprocessor changes theimage orientation by flipping the image vertically or horizontally or byrotating the image.
 158. The apparatus according to claim 150 whereinthe preprocessor attaches sensor information to the data representingthe image.
 159. The apparatus according to claim 150 wherein thepreprocessor applies a watermark to the data representing the image.160. The apparatus according to claim 159 wherein the watermark includesinformation used for tamper-proofing the image to allow for identifyinga modified image or modified information associated with the image. 161.An apparatus for processing an image of a biometric, comprising:gradient edge detection means for detecting features in a biometricbased on data representing an image of at least a portion of thebiometric; means for modeling the image as a function of the features;and means for displaying the image to a user with an overlay of theindications of the biometric features of the image.
 162. A method forprocessing an image of a biometric, comprising: acquiring datarepresenting features of an image of at least a portion of a biometricusing a data acquisition unit; and forming a model of the image usingthe features of the biometric for at least two resolutions using amodeler wherein the resolutions include an outline model at a lowresolution, a details model at a high resolution, and a fine detailsmodel used to locate and define particular biometric features moreaccurately than at a low or high resolution.
 163. The method accordingto claim 162 wherein the biometric is an area of skin with a frictionridge pattern, the outline model includes edge topology of ridgefeatures, and the details model includes edge topology and specifics ofridge deviations and locations and sizes of pores.
 164. The methodaccording to claim 162 wherein forming a model of the image includesapplying a gradient edge detection process.
 165. The method according toclaim 162 wherein the biometric includes at least one of the following:ear shape and structure, facial or hand thermograms, iris or retinastructure, handwriting, fingerprints, palm prints, foot prints, or toeprints.
 166. The method according to claim 162 further including storingthe image and model in a database with associated information, includingat least one of the following: identity of a person associated with thebiometric; manufacturer, model, or serial number of the instrumentsupplying the data representing the portion of the biometric; date ofimaging the biometric; time day of imaging the biometric; calibrationdata associated with the instrument used to acquire the image;temperature at the time of acquiring the image; photograph, voicerecording, or signature of the person whose biometric is imaged;watermark; unique computer ID of the computer receiving the datarepresenting the image of the biometric; or name of person logged intothe computer at the time of acquiring the image.
 167. The methodaccording to claim 162 further including comparing a previously storedmodel from a database to a present image.
 168. The method according toclaim 167 wherein the present image is at least a portion of a biometricof a person having a known identity or having an unknown identity. 169.The method according to claim 167 wherein, if a candidate match ofoutline features is found, the method further includes comparing detailsfeatures of the previously stored model with details features of thepresent image.
 170. The method according to claim 167 wherein thecomparing includes adaptively conforming the previously stored model toaccount for variability associated with recording or acquiring thepresent image.
 171. The method according to claim 162 further includingpreprocessing the data, wherein the preprocessing includes at least oneof the following: subsampling the at least a portion of the biometric toproduce the data representing the image; decimating the datarepresenting the image; binning the data representing the image;correcting for uneven imaging of the at least a portion of thebiometric; accounting for defective pixels of an instrument used toacquire the at least a portion of the biometric; encrypting the datarepresenting the image; changing the image orientation by flipping theimage vertically or horizontally or by rotating the image; attachingsensor information to the data representing the image; or applying awatermark to the data representing the image.
 172. An apparatus forprocessing an image of a biometric, comprising: a data acquisition unitconfigured to acquire data representing an image of at least a portionof a biometric; and a modeler configured to form a model of features ofthe biometric for at least two resolutions wherein the resolutionsinclude an outline model at a low resolution, a details model at a highresolution, and a fine details model configured to locate and defineparticular biometric features more accurately than at a low or highresolution.
 173. The apparatus according to claim 172 wherein thebiometric is an area of skin with a friction ridge pattern, the outlinemodel includes edge topology of ridge features, and the details modelincludes edge topology and specifics of ridge deviations and locationsand sizes of pores.
 174. The apparatus according to claim 172 whereinthe modeler includes a gradient edge detector to detect biometricfeatures in the image.
 175. The apparatus according to claim 172 whereinthe biometric includes at least one of the following: ear shape andstructure, facial or hand thermograms, iris or retina structure,handwriting, fingerprints, palm prints, or toe prints.
 176. Theapparatus according to claim 172 further including a database thatstores the image and model with associated information, including atleast one of: identity of a person associated with the biometric;manufacturer, model, or serial number of the instrument supplying thedata representing the portion of the biometric, date of imaging thebiometric; time of day of imaging the biometric; calibration dataassociated with the instrument used to acquire the image; temperature atthe time of acquiring the image; photograph, voice recording, orsignature of the person whose biometric is imaged; watermark; uniquecomputer ID of the computer receiving the data representing the imagefrom the instrument acquiring the image of the biometric; or name ofperson logged into the computer at the time of acquiring the image. 177.The apparatus according to claim 172 further including a comparison unitthat compares a previously stored model from a database to a presentimage.
 178. The apparatus according to claim 177 wherein the presentimage is at least a portion of a biometric of a person having a knownidentity or having an unknown identity.
 179. The apparatus according toclaim 177 wherein, if a candidate match of outline features is found,the comparison unit compares details features of the previously storedmodel with details features of the present image.
 180. The apparatusaccording to claim 177 wherein the comparison unit adaptively conformsthe previously stored model to account for variability associated withrecording or acquiring the present image.
 181. The method according toclaim 172, further including a preprocessor that preprocesses the data,the preprocessor including at least one of the following components: asubsampler that subsamples the at least a portion of the biometric toproduce the data representing the image; a decimator that decimates thedata representing the image; a binning unit that bins the datarepresenting the image; a field flattener that corrects for unevenimaging of the at least a portion of the biometric; a defective pixelcorrection unit that accounts for defective pixels of an instrument usedto acquire the at least a portion of the biometric; an encryption unitthat encrypts the data representing the image; an image orientation unitthat changes the orientation by flipping the image vertically orhorizontally or by rotating the image; a sensor data application unitthat attaches sensor information to the data representing the image; ora watermark application unit that applies a watermark to the datarepresenting the image.
 182. An apparatus for processing an image of abiometric, comprising: means for acquiring data representing an image ofat least a portion of a biometric; and means for forming a model offeatures of the biometric for at least two resolutions wherein theresolutions include an outline model at a low resolution, a detailsmodel at a high resolution, and a fine details model configured tolocate and define particular biometric features more accurately than ata low or high resolution.